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Brain-Computer Interface: Current and Emerging Rehabilitation Applications
Archives of Physical Medicine and Rehabilitation journal homepage: www.archives-pmr.org Archives of Physical Medicine and Rehabilitation 2015;96(3 Suppl 1):S1-7
INTRODUCTION
Brain-Computer Interface: Current and Emerging Rehabilitation Applications Janis J. Daly, PhD, MS,a,b,c,d Jane E. Huggins, PhDe From the aBrain Rehabilitation Research Program, McKnight Brain Institute, University of Florida, Gainesville, FL; bDepartment of Neurology, College of Medicine, University of Florida, Gainesville, FL; cBrain Rehabilitation Research Center of Excellence; dNorth Florida/South Georgia Veterans Affairs Medical Center, Gainesville, FL; and eDepartment of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, and Program of Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI.
Abstract A formal definition of brain-computer interface (BCI) is as follows: a system that acquires brain signal activity and translates it into an output that can replace, restore, enhance, supplement, or improve the existing brain signal, which can, in turn, modify or change ongoing interactions between the brain and its internal or external environment. More simply, a BCI can be defined as a system that translates “brain signals into new kinds of outputs.” After brain signal acquisition, the BCI evaluates the brain signal and extracts signal features that have proven useful for task performance. There are 2 broad categories of BCIs: implantable and noninvasive, distinguished by invasively and noninvasively acquired brain signals, respectively. For this supplement, we will focus on BCIs that use noninvasively acquired brain signals. Archives of Physical Medicine and Rehabilitation 2015;96(3 Suppl 1):S1-7 ª 2015 by the American Congress of Rehabilitation Medicine
A formal definition of brain-computer interface (BCI) is as follows: “a system that measures central nervous system (CNS) activity and converts it into artificial output that replaces, restores, enhances, supplements, [informs], or improves natural CNS output and thereby changes the ongoing interactions between the CNS and its external or internal environment.”1(p3) More simply, a BCI can be defined as a system that translates “brain signals into new kinds of outputs.”1(p5) There are 2 major ways in which BCIs can be used. The first is straightforward and has been studied for >25 years; in this case, the BCI system acquires a brain signal and allows the user, through feedback, to engage the BCI output for control of the environment (light switch, temperature control) or communication devices. A second and newly emerging BCI application involves using the system as a motor learningeassist device. In this case, the BCI may enhance motor control recovery by demanding more focused attention or guiding activation or deactivation of brain signals.2 BCI research has experienced a recent exponential growth, which can be attributed to a number of the following factors: availability of rapid, real-time sophisticated signal processing methods; a greater understanding of the characteristics and uses of brain signals; an Presented to the National Institutes of Health, National Science Foundation, and other organizations (for a full list, see http://bcimeeting.org/2013/sponsors.html), June 3-7, 2013, Asilomar Conference Grounds, Pacific Grove, CA. Disclosures: none.
appreciation of the phenomenon of activity-dependent brain plasticity; and a growing dissatisfaction with current rehabilitation methods and the need for improved methods for recovery of function for those with persistent motor impairment.2 Brain signals can be acquired in a number of forms, including electrical (eg, electroencephalography [EEG]) or magnetic fields (eg, functional magnetic resonance imaging [fMRI]) or functional near infrared spectroscopy (fNIRS). It is important for those of us who are clinicians and clinicianscientists to be informed about the development and capability of BCIs because these systems have potential to enhance rehabilitation methods. Even more importantly, it is critical for us to participate in the design and development of these systems so that BCI system designs are grounded in the needs of patients, framed within feasible technical interfaces, and constructed for practical delivery in a clinical environment. To that end, we present this supplement: BrainComputer Interface: Current and Emerging Rehabilitation Applications. Within this supplement, we are providing articles that arose from presentations at the 2013 International Brain-Computer Interface Meeting, which was held June 3 through June 7, 2013, at the Asilomar Conference Center in Pacific Grove, California. The 2013 BCI meeting was the fifth in the international BCI meeting series, with past meetings in 1999, 2002, 2005, and 2010. The purpose of the international BCI meeting series is to bring
0003-9993/15/$36 - see front matter ª 2015 by the American Congress of Rehabilitation Medicine http://dx.doi.org/10.1016/j.apmr.2015.01.007
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J.J. Daly, J.E. Huggins
together the diverse contributors to BCI research and development in a distinctive retreat-style meeting that encourages interaction, collaboration, and discussion. Therefore, the international BCI meeting series strives to push the BCI field forward, encouraging growth and translation of BCIs from the laboratory to the clinic. The 2013 meeting was supported by the National Institutes of Health and National Science Foundation and other governmental and private sponsors (further sponsor information available at http://bcimeeting. org/2013/sponsors.html). The meeting drew scientists from 29 countries, representing 165 research groups, with a total of 301 attendees, of whom 37% were students or postdoctoral fellows. There were >200 extended abstracts submitted for peer review, from which 25 were selected for oral presentation (individual index abstracts: http://bcimeeting.org/2013/researchsessions.html), and 181 were selected for poster presentation (individual index abstracts: http:// bcimeeting.org/2013/posters.html). Accepted abstracts were published in open-access conference proceedings (http://castor.tugraz.at/ doku/BCIMeeting2013/BCIMeeting2013_all.pdf). The retreat-style format featured 19 highly interactive workshops3 and an exhibit hall with formal poster session and technology demonstrations. The 2013 BCI meeting theme was Defining the Future. Compared with prior BCI conferences, attendance included an increased representation of clinicians, clinician-scientists, and people with disabilities. There were a number of firsts for the meeting series. First, the planning committee was composed of BCI researchers from around the world. Second, both the planning committee and conference participants included people with severe disabilities who need assistive technology for communication. A woman with amyotrophic lateral sclerosis (ALS) who uses assistive technology for communication served on the program committee (http:// bcimeeting.org/2013/meetinginfo.html). She attended the meeting remotely; she participated in a panel discussion and provided a presentation at a virtual BCI user’s forum. This forum provided a venue by which BCI users could speak directly to the conference attendees. A man with brainstem stroke attended the meeting with his caregivers, presenting both in a workshop and in the virtual user’s forum. Both are coauthors on an article in this supplement4 summarizing the experiences of BCI users with the current state of BCI technology. A third new development was that attendees at the 2013 BCI meeting voted to establish a Brain-Computer Interface Society, which will plan and oversee the 6th International BCI Meeting to be held in 2016. Fourth, there was an increase in the number of venues for dissemination of results. This supplement contains articles with a clinical or patient experience focus. A special section in the Journal of Neural Engineering published articles with an engineering focus.5 A summary of the conference workshops was published in the newly established Brain-Computer Interfaces journal.3 The articles in this supplement provide examples of work conducted using a variety of BCI technology applications, including communication, leisure activities, and motor learning.
List of abbreviations: AAC ALS BCI CNS EEG fMRI fNIRS tDCS TMS
augmentative and alternative communication amyotrophic lateral sclerosis brain-computer interface central nervous system electroencephalography functional magnetic resonance imaging functional near infrared spectroscopy transcranial direct current stimulation transcranial magnetic stimulation
Communication Problem Communication is an essential function for health care,6,7 function, and quality of life.8 For those with neuromuscular impairments and difficulty with writing or speaking, augmentative and alternative communication (AAC) devices can compensate and provide device-assisted communication.6 Most currently available AAC devices are controlled by available physical movements, and in the presence of volitional movement, they work well for performing a simple task. However, there are limitations to currently available AAC devices. First, the capability of currently available AAC devices can be overwhelmed by task complexity or by the simultaneous task demands of a given function. Second, some individuals do not possess the required physical capability to control an AAC device, and others have progressive diseases which eventually preclude their use of any physical movement to control communication devices. Therefore, the inability of some people to operate AAC devices is of particular concern6 and represents an area of vital need. A study on end-of-life decisions by people with ALS7 quoted a participant as saying “as long as I can properly communicate with my voice, my eyes or a machine or whatever, I want to have a respirator.But as soon as I can no longer communicate, that’s it! I don’t want anything else to be done.”(p210)
Role of BCIs in rehabilitation In contrast with most available AAC devices, BCIs can be controlled through the direct use of brain signal, bypassing the need for volitional muscle activity as a control paradigm. For a number of years,9,10 potential BCI users and caregivers have expressed the importance of BCIs for communication. In a focus group of potential BCI users with ALS and their caregivers, one caregiver described the promise of BCI as follows: “I just think it is wonderful that you can give someone a voice who is losing theirs.”11(p523) BCIs have been developed and tested for use in controlling devices for communication. BCIs are most appropriate and most needed by people with few other options for control of assistive technology. These include people with late-stage ALS, people with disorders of consciousness who show signs of cognitive awareness but lack other means of communication, and other populations of people who cannot reliably operate physical interfaces or eye gaze systems to access assistive technology. In this supplement, Ku¨bler et al12 discuss a decision process for determining who should participate in in-home BCI research studies, considering participant characteristics, support structure, and environmental factors. To date, BCIs have primarily been used in the laboratory or in controlled research studies. However, one commercial BCI device is now available.a Therefore, as discussed by Hill et al13 in this supplement, critical issues remain for the widespread adoption of BCIs as practical AAC devices for clinical use. Peters’ article4 describes both the promise and shortcomings of BCI as a communication device and compares BCI to conventional assistive technology solutions. BCIs are increasingly being integrated with other commercial assistive technology on an experimental basis14 and can form an interface that is based on brain signals and is incorporated within the framework of other existing assistive technology, increasing the accessibility of such devices.15 There is a growing awareness that BCIs can be used in combination with physical input signals if the patient has such signals available, a concept described as a hybrid BCI design.16 In this supplement, Schettini et al17 investigate the www.archives-pmr.org
Brain-computer interface: current and emerging applications design of a hybrid BCI to provide people with ALS with a system that will adapt to meet their needs as their ALS progresses. People with ALS have been the predominate user group for noninvasive BCI communication systems to date,15,18-20 and demonstrations of home use have been published.21,22 Hill et al13 draw on examples from a study of in-home BCI use by people with ALS, and most of the participants in the virtual BCI user’s forum had ALS.4 For patients diagnosed with disorders of consciousness, BCI may offer the only opportunity of demonstrating awareness. A recent review showed that 4 (17%) of 24 patients who had been diagnosed as being in a vegetative state were not only consciously aware but could answer yes or no questions.23 BCIs offer a method to evaluate awareness and to restore a communication channel. BCI use for disorders of consciousness is an emerging field of great importance. Studies in this area include BCIs using auditory-, tactile-, and motor imageryebased designs.23-32 In this supplement, Coyle et al33 describe a study of BCI use with people in a minimally conscious state, demonstrating that it is possible to learn to use a BCI device even in the absence of any other method of communication. People with spinal cord injury or brainstem stroke represent other populations for whom BCIs may be important and useful. BCI studies with people with spinal cord injury have included both computer access tasks34,35 and exoskeleton control.35 In this supplement, Huggins et al explore the BCI design priorities for people with SCI for the purpose of setting research goals and evaluating how close BCIs are to providing benefit for this population. Also, Riccio et al,36 in this supplement, evaluated a hybrid BCI design using a BCI in conjunction with a muscle activity switch for error correction in a study that included people with brainstem stroke.
Limitations The primary concern for BCIs as an AAC is the reliance of most BCIs on visual presentation of options, with an associated concern about possible reliance on eye gaze.37 Indeed, some people with ALS have trouble not only controlling eye gaze, but also with keeping their eyes open,38 which poses potential problems in using a visual display to view options and feedback results. To address this problem, BCIs that operate with covert attention have been developed, proving that eye gaze control is not required for BCI use.39-43 However, covert attention is associated with lower accuracy of BCI performance.44 A BCI with a visual display has even been designed for use in an eyes-closed condition by using lights that are visible through the eyelids.45 Alternative BCI designs using auditory32,43,46-49 and even tactile50-53 displays of information are under development. In this issue, auditory BCI feedback is mentioned by Ku¨bler et al12 as part of the considerations for selecting BCI participants, and it is also used in Coyle’s study. One other consideration is the ability to learn to use a BCI. There have been some reports of greater variability in success at learning to use a BCI device among those with disabilities compared with nonimpaired adults.54-56 However, most people are able to learn to use at least one of the BCI designs for communication.57,58 Overall, BCIs for communication have been successful in the laboratory and for long-term functional use.22,59
Leisure activities Problem Disability touches every aspect of human life; therefore, assistive technology is needed to provide access to all types of activities www.archives-pmr.org
S3 that contribute to quality of life.60 Assistive technology can enable participation in sport and leisure activity, providing connection to others and improvements in self-esteem and self-confidence.61,62
Potential BCIs have the potential to provide an array of leisure pursuits, and applications in this area are just emerging. BCIs have enough flexibility to provide useful computer access, which can be used for leisure activities.15 Peters et al4 describe the need for this type of flexibility, as articulated by potential BCI users. In Huggins’ survey,63 respondents also suggested game playing as a desirable BCIassisted activity. We can also note that some leisure activities are served by existing BCIs, which provide for environmental control functions (eg, television control). BCIs based on sensorimotor brain activity have used games as part of user training protocols.64 BCI applications for leisure activities have also been developed either as primary applications or additional functions for BCIs.65-67 In this supplement and elsewhere, Holz et al67 describe the positive impact of a BCI-operated painting program.
Recovery of function Problem It is important to develop innovative methods (eg, BCI neural feedback systems) to restore motor function to those with neural injury or disease because current standard care and research approaches do not restore normal function. Generally, there are 2 research approaches currently studied for treating motor impairment after neurologic injury or disease: peripherally directed treatments (eg, exercise, exercise-assisted technologies68-73) or centrally directed treatments involving brain stimulation methods, such as transcranial magnetic stimulation (TMS),74,75 transcranial electrical stimulation,75 or transcranial direct current stimulation (tDCS).75,76 Peripherally directed interventions are promising, for example, for stroke survivors. Interventions tested include robotics,69 functional electrical stimulation,71,73 coordination and functional task training,68,71,73 and body weightesupported treadmill training.70,71 Even for those with moderate to severe impairment, some studies show clinically and statistically significant gains in response to innovative treatment in the Fugl-Meyer lower-limb coordination scale71 and the Arm Motor Ability Test function score73 and significant gains in quality of life73 or in proxy function measures (eg, gait speed).70 Although promising, these peripherally directed interventions do not restore normal function in most study participants, and some participants do not respond to treatment. Therefore, it is critical to develop other more effective treatment methods. Although less studied than peripherally directed interventions, brain stimulation methods have been more recently applied to the problem of motor relearning for those with dyscoordination from neural injury or disease. The motor skill acquisition process (motor learning) is driven by neuroplasticity in a number of cortical centers, including the motor cortex,76,77 cerebellum,78-80 and cerebellar pathways through the thalamus to the cortex.81 Engagement of these neural centers and pathways during the motor learning process is thought to produce a motor memory, or motor engram, which includes the neural substrate engaged during the task.76 Brain stimulation methods hold some promise. However, as applied to the problem of motor relearning after neural injury or disease, results are currently mixed. In the study of stroke treatment, scientists are reporting transient (days to a few weeks) motor gains in
S4 response to the application of TMS, but it is difficult to evaluate the body of literature because of heterogeneity of both subject and intervention variables. These include time since stroke (acute/ chronic); repetitive TMS frequency variability (1e25Hz repetitive TMS); treatment duration variability (1e10 sessions); and measures of motor function for which the value is unknown regarding the minimally clinically significant improvement.74,82,83 In the application of tDCS, results are generally positive for improving motor function in older adults84,85 and stroke survivors.86,87 tDCS application for those with Parkinson disease ranges from mixed88 to no response on most motor measures.89 In this supplement, Ang et al90 present a study of the effects of tDCS on brain signals during training with an EEG-BCI; findings suggest that tDCS can enhance brain signal features that are used during EEG-BCI training, but further work will be needed to determine the actual role and benefit of both EEG-BCI and tDCS in physical recovery. Similar to the difficulties in evaluating response to rTMS treatment, the same or similar difficulties exist in evaluating response to tDCS. Further, reported results are not currently greater than peripherally directed treatments. Some have proposed that the problem arises from the variability of neural networks across individuals, leading to a lack of certainty as to whether the applied stimulus did not work well in general or only for a given individual (who did not exhibit change).75 One suggestion put forth for future work75 is to combine the new information regarding modeling the electrical fields induced by brain stimulation91 and the emerging information of cortical circuits92 to more specifically and accurately target brain stimulation.
Justification Given the promise of direct brain stimulation, but its limited results to date, it is important to continue to develop interventions for motor recovery that more directly target or engage the brain in the location where the pathology occurs. Given the individual variability of brain structure, neural networks, and alterations after brain injury or disease, one promising avenue of working directly with the brain itself is to use a neural feedback training system, which engages the individual’s central nervous system encompassing all its individual uniqueness. Biofeedback has been a useful intervention for a number of different types of health problems. Biofeedback systems can be based on noninvasively acquired brain signals, such as real-time fMRI, fNIRS, and EEG. The term “BCI” has been applied to these noninvasive biofeedback systems, which are dependent upon rapid brain signal processing that can be used in real time during training for cognition, emotion function, and motor learning; that is, no timeconsuming, off-line signal processing is conducted and motor learning fMRI-BCI has been tested in several therapeutic applications. For example, fMRI-BCI was used in a preliminary study to train those with schizophrenia to more normally control emotion through modulation of the left and right insula brain regions.93 Additionally, real-time fMRI-BCI was successfully used to reduce pain perception through modulation of the right anterior cingulate cortex.94 Studies of real-time fNIRS-BCI for application to motor function have focused on identification of usable signals in a BCI neural feedback system.95,96 EEG-BCI systems have been successfully used to reduce seizure activity97,98 and improve function in those with attention deficit hyperactivity disorder.99 With mixed success, EEG-BCI systems have been tested for use in motor learning after stroke.100,101 Recently, a randomized controlled trial was conducted to compare physical therapy arm coordination/ functional test training alone or in combination with EEG-BCI
J.J. Daly, J.E. Huggins training.102 Results showed a statistically significant additive advantage of the EEG-BCI treatment, according to the Fugl-Meyer arm coordination score; however, the absolute group difference was only 3 Fugl-Meyer arm coordination score points, which is not clinically significant.103 The Marone study,104 in this supplement, looks to the future with a study to test the feasibility of using noninvasive EEG-BCI systems in the clinic.
Future challenges and directions Although EEG-BCI proved clinically feasible for motor training, other factors must first be considered before clinical deployment should or can be realized, with efficacy of treatment being the foremost unproven factor. There are many sources of difficulty in developing an efficacious noninvasive brain neural feedback system. For example, real-time fMRI-BCI is not practical for daily biofeedback sessions. Real-time fNIRS-BCI and EEG-BCI are more practical and portable, but they depend on brain signals that are a summation of complex neural activity: the summated signals obfuscate the signal features that are likely most desirable and most reflective of the neural activity that should be targeted for treatment. In response to this difficulty, there is a growing body of literature that seeks to derive more specific meaning from EEG signals so that EEG-BCI can be more effectively applied to training recovery of motor function after neural injury or disease.105-109 At the same time, the future development of BCI feedback systems should leverage knowledge of the neural mechanisms of motor learning and how to engage those mechanisms specifically with BCI neural feedback systems. We can learn this process from the example provided in the study of tDCS brain stimulation. That is, Orban de Xivry and Shadmehr72 have synthesized a body of work and derived 3 major principles of tDCS and treatment responses as follows: (1) “Firing rates are increased by tDCS anodal polarization and decreased by cathodal polarization, accounting for the effect of tDCS on motor behaviors that do not involve learning”(p3381); (2) Anodal polarization strengthens newly formed associations, accounting for facilitation by tDCS of sequence-learning110 and “speed/accuracy trade-off skill,”111 requiring the formation of new patterns of motor activity”; and (3) “tDCS polarization modulates the memory of new/ preferred firing patterns, accounting for tDCS alteration of the rate of motor adaption.”112,113 Theories have been put forth to explain some of the positive response to tDCS; these theories include modulation of long-term potentiation,114 a decrease in gammaaminobutyric acid,115 and an increase or decrease in the level of noise in neural activity.116 However, none of these theories alone adequately explain the experimental results. Once proven, mechanisms of motor learning may form the basis for a BCI that could deliver neural regulators to a particular brain location or to specific neural substrates to enhance motor skill acquisition after neural injury or disease. Although seemingly impossible, others117 have recently designed and tested an EEG-BCI that uses the brain state in a mouse model to control a wireless-powered optogenetic designer cell implant for ultimate induction of synthetic interferon-beta (balances the expression of pro- and antiinflammatory agents in the brain and protects the brain). The application of brain stimulation and the use of brain neural feedback training both depend on our knowledge of motor skill acquisition, the function of the neural centers involved, the mechanisms of the interventions, and the mechanisms of neural recovery of motor control. The synthesis of these discoveries will provide the platform for future BCIs. www.archives-pmr.org
Brain-computer interface: current and emerging applications
Conclusions BCIs are emerging as interventions to restore function through assistive technology and to promote recovery. Success in these applications has been observed in the laboratory and in limited, in-home studies for communication applications. However, BCI technology is still clinically immature, and further development with the participation of clinicians and clinician-scientists is needed before the full potential of BCIs for rehabilitation can be realized.
Supplier a. IntendiX; g.tec Medical Engineering GmbH.
Keywords Brain-computer interfaces; Rehabilitation
Corresponding author Janis J. Daly, PhD, MS, Director, Brain Rehabilitation Research Center of Excellence, North Florida/South Georgia Veterans Affairs Medical Center, 151-A, 1601 SW Archer Rd, Gainesville, FL 32608. E-mail address: [email protected].
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S6 35. Enzinger C, Ropele S, Fazekas F, et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp Brain Res 2008;190:215-23. 36. Riccio A, Holz E, Arico` P, et al. Hybrid p300-based brain-computer interface to improve usability for people with severe motor disability: electromyographic signals for error correction during a spelling task. Arch Phys Med Rehabil 2015;96:S54-61. 37. Brunner P, Joshi S, Briskin S, Wolpaw JR, Bischof H, Schalk G. Does the P300 speller depend on eye-gaze? J Neural Eng 2010;7:056013. 38. Hayashi H, Oppenheimer EA. ALS patients on TPPV: totally lockedin state, neurologic findings and ethical implications. Neurology 2003;61:135-7. 39. Treder M, Blankertz B. (C)overt attention and visual speller design in an ERP-based brain-computer interface. Behav Brain Funct 2010;6:28. 40. Aloise F, Arico P, Schettini F, Salinari S, Mattia D, Cincotti F. Asynchronous gaze-independent event-related potential-based braincomputer interface. Artif Intell Med 2013;59:61-9. 41. Marchetti M, Piccione F, Silvoni S, Gamberini L, Priftis K. Covert visuospatial attention orienting in a brain-computer interface for amyotrophic lateral sclerosis patients. Neurorehabil Neural Repair 2013;27:430-8. 42. Walter S, Quigley C, Andersen SK, Mueller MM. Effects of overt and covert attention on the steady-state visual evoked potential. Neurosci Lett 2012;519:37-41. 43. Riccio A, Mattia D, Simione L, Olivetti M, Cincotti F. Eye-gaze independent EEG-based brain-computer interfaces for communication. J Neural Eng 2012;9:045001. 44. Arico P, Aloise F, Schettini F, Salinari S, Mattia D, Cincotti F. Influence of P300 latency jitter on event related potential-based braincomputer interface performance. J Neural Eng 2014;11:035008. 45. Lim JH, Hwang HJ, Han CH, Jung KY, Im CH. Classification of binary intentions for individuals with impaired oculomotor function: ‘Eyes-closed’ SSVEP-based brain-computer interface (BCI). J Neural Eng 2013;10:026021. 46. Hill NJ, Scholkopf B. An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli. J Neural Eng 2012;9:026011. 47. Hohne J, Schreuder M, Blankertz B, Tangermann M. A novel 9-class auditory ERP paradigm driving a predictive text entry system. Front Neurosci 2011;5:99. 48. Nijboer F, Furdea A, Gunst I, et al. An auditory brain-computer interface (BCI). J Neurosci Methods 2008;167:43-50. 49. Schreuder M, Rost T, Tangermann M. Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Front Neurosci 2011;5:112. 50. Brouwer A-M, Van Erp Jan BF. A tactile P300 brain-computer interface. Front Neurosc 2010;5:12. 51. Ortner R, Rodriguez J, Guger C. A tactile P300 brain-computer interface. Austria: g.tec Medical Engineering; 2012. Technical Report 3. 52. Kaufmann T, Holz EM, Kubler A. Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state. Front Neurosci 2013;7:129. 53. Mori H, Matsumoto Y, Struzik ZR, et al. Multi-command tactile and auditory brain computer interface based on head position stimulation. In: Proceedings of the 5th International Brain-Computer Interface Meeting; 2013 June 3-7; Pacific Grove, CA. Graz (Austria): Graz Univ of Technology. Available at: http://castor.tugraz.at/doku/ BCIMeeting2013/095.pdf. Accessed February 10, 2015. 54. Piccione F, Giorgi F, Tonin P, et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol 2006;117:531-7. 55. Sellers EW, Kubler A, Donchin E. Brain-computer interface research at the university of south florida cognitive psychophysiology laboratory: the P300 speller. IEEE Trans Neural Syst Rehabil Eng 2006;14:221-4. 56. Ortner R, Aloise F, Pruckl R, et al. Accuracy of a P300 speller for people with motor impairments: a comparison. Clin EEG Neurosci 2011;42:214-8.
J.J. Daly, J.E. Huggins 57. Guger C, Edlinger G, Harkam W, Niedermayer I, Pfurtscheller G. How many people are able to operate an EEG-based brain-computer interface (BCI)? IEEE Trans Neural Syst Rehabil Eng 2003;11: 145-7. 58. Guger C, Daban S, Sellers E, et al. How many people are able to control a P300-based brain-computer interface (BCI)? Neurosci Lett 2009;462:94-8. 59. Hill K, Kovacs T, Shin S. Reliability of brain computer interface language sample transcription procedures. J Rehabil Res Dev 2014; 51:579-90. 60. Cook AM, Hussey SM. Assistive technologies: principles and practice. 2nd ed. St Louis: Mosby2002. 61. McCarty E, Morress C. Establishing access to technology: an evaluation and intervention model to increase the participation of children with cerebral palsy. Phys Med Rehabil Clin N Am 2009;20:523-34. 62. Slater D, Meade MA. Participation in recreation and sports for persons with spinal cord injury: review and recommendations. NeuroRehabilitation 2004;19:121-9. 63. Huggins JE, Moinuddin AA, Chiodo AE, Wren PA. What would brain-computer interface users want: opinions and priorities of potential users with spinal cord injury. Arch Phys Med Rehabil 2015; 96:S38-45. 64. Scherer R, Proll M, Allison B, Muller-Putz GR. New input modalities for modern game design and virtual embodiment. In: Proceedings of the IEEE Virtual Reality Conference; 2012 March 4-8; Costa Mesa, CA. p 163-4. Available at: http://ieeexplore.ieee.org/xpl/mostRecentIssue. jsp?punumber=6179238. Accessed February 10, 2015. 65. Mason SG, Bohringer R, Borisoff JF, Birch GE. Real-time control of a video game with a direct brainecomputer interface. J Clin Neurophysiol 2004;21:404-8. 66. Hasan BA, Gan JQ, Hangman BCI. an unsupervised adaptive selfpaced brain-computer interface for playing games. Comput Biol Med 2012;42:598-606. 67. Holz EM, Botrel L, Kaufmann T, Ku¨bler A. Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study. Arch Phys Med Rehabil 2015;96:S16-26. 68. Wolf SL, Winstein CJ, Miller JP, et al. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA 2006;296: 2095-104. 69. Lo AC, Guarino P, Krebs HI, et al. Multicenter randomized trial of robot-assisted rehabilitation for chronic stroke: methods and entry characteristics for VA ROBOTICS. Neurorehabil Neural Repair 2009;23:775-83. 70. Duncan PW, Sullivan KJ, Behrman AL, et al. Body-weight-supported treadmill rehabilitation after stroke. N Engl J Med 2011;364:2026-36. 71. Daly JJ, Zimbelman J, Roenigk KL, et al. Recovery of coordinated gait: randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training. Neurorehabil Neural Repair 2011;25:588-96. 72. Pundik S, Holcomb J, McCabe J, Daly JJ. Enhanced life-role participation in response to comprehensive gait training in chronic stroke survivors. Disabil Rehabil 2012;34:2264-71. 73. McCabe J, Monkiewicz M, Holcomb J, Pundik S, Daly JJ. Comparison of robotics, FES, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil 2014 Nov 15. [Epub ahead of print] 74. Tang IN. The effect of repetitive transcranial magnetic stimulation on upper extremity motor function in stroke patients: a meta-analytical review. J Food Drug Anal 2012;20:1-5. 75. Di Lazzaro V, Rothwell JC. Corticospinal activity evoked and modulated by non-invasive stimulation of the intact human motor cortex. J Physiol 2014;592:4115-28. 76. Orban de Xivry JJ, Shadmehr R. Electrifying the motor engram: effects of tDCS on motor learning and control. Exp Brain Res 2014; 232:3379-95.
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Brain-computer interface: current and emerging applications 77. Orban de Xivry JJ, Criscimagna-Hemminger SE, Shadmehr R. Contributions of the motor cortex to adaptive control of reaching depend on the perturbation schedule. Cereb Cortex 2011;21:1475-84. 78. Smith MA, Shadmehr R. Intact ability to learn internal models of arm dynamics in huntington’s disease but not cerebellar degeneration. J Neurophysiol 2005;93:2809-21. 79. Izawa J, Criscimagna-Hemminger SE, Shadmehr R. Cerebellar contributions to reach adaptation and learning sensory consequences of action. J Neurosci 2012;32:4230-9. 80. Gibo TL, Criscimagna-Hemminger SE, Okamura AM, Bastian AJ. Cerebellar motor learning: are environment dynamics more important than error size? J Neurophysiol 2013;110:322-33. 81. Chen H, Hua SE, Smith MA, Lenz FA, Shadmehr R. Effects of human cerebellar thalamus disruption on adaptive control of reaching. Cereb Cortex 2006;16:1462-73. 82. Le Q, Qu Y, Tao Y, Zhu S. Effects of repetitive transcranial magnetic stimulation on hand function recovery and excitability of the motor cortex after stroke: a meta-analysis. Am J Phys Med Rehabil 2014; 93:422-30. 83. Kandel M, Beis JM, Le Chapelain L, Guesdon H, Paysant J. Noninvasive cerebral stimulation for the upper limb rehabilitation after stroke: a review. Ann Phys Rehabil Med 2012;55:657-80. 84. Zimerman M, Nitsch M, Giraux P, Gerloff C, Cohen LG, Hummel FC. Neuroenhancement of the aging brain: restoring skill acquisition in old subjects. Ann Neurol 2013;73:10-5. 85. Hardwick RM, Celnik PA. Cerebellar direct current stimulation enhances motor learning in older adults. Neurobiol Aging 2014;35:2217-21. 86. Khedr EM, Shawky OA, El-Hammady DH, et al. Effect of anodal versus cathodal transcranial direct current stimulation on stroke rehabilitation: a pilot randomized controlled trial. Neurorehabil Neural Repair 2013;27:592-601. 87. Lefebvre S, Thonnard JL, Laloux P, Peeters A, Jamart J, Vandermeeren Y. Single session of dual-tDCS transiently improves precision grip and dexterity of the paretic hand after stroke. Neurorehabil Neural Repair 2014;28:100-10. 88. Benninger DH, Lomarev M, Lopez G, et al. Transcranial direct current stimulation for the treatment of Parkinson’s disease. J Neurol Neurosurg Psychiatry 2010;81:1105-11. 89. Verheyden G, Purdey J, Burnett M, Cole J, Ashburn A. Immediate effect of transcranial direct current stimulation on postural stability and functional mobility in Parkinson’s disease. Mov Disord 2013;28:2040-1. 90. Ang KK, Guan C, Phua KS, et al. Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation. Arch Phys Med Rehabil 2015;96:S79-87. 91. Thielscher A, Opitz A, Windhoff M. Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation. Neuroimage 2011;54:234-43. 92. Nummenmaa A, McNab JA, Savadjiev P, et al. Targeting of white matter tracts with transcranial magnetic stimulation. Brain Stimul 2014;7:80-4. 93. Ruiz S, Lee S, Soekadar SR, et al. Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum Brain Mapp 2013;34:200-12. 94. deCharms RC, Maeda F, Glover GH, et al. Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A 2005;102:18626-31. 95. Schurholz M, Rana M, Robinson N, et al. Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS. Conf Proc IEEE Eng Med Biol Soc 2012;2012:4728-31. 96. Rea M, Rana M, Lugato N, et al. Lower limb movement preparation in chronic stroke: a pilot study toward an fNIRS-BCI for gait rehabilitation. Neurorehabil Neural Repair 2014;28:564-75. 97. Walker JE, Kozlowski GP. Neurofeedback treatment of epilepsy. Child Adolesc Psychiatr Clin N Am 2005;14:163-76. viii. 98. Kotchoubey B, Strehl U, Uhlmann C, et al. Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 2001;42:406-16.
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S7 99. Monastra VJ, Lynn S, Linden M, Lubar JF, Gruzelier J, LaVaque TJ. Electroencephalographic biofeedback in the treatment of attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback 2005;30:95-114. 100. Ang KK, Guan C, Chua KS, et al. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery braincomputer interface with robotic feedback. Conf Proc IEEE Eng Med Biol Soc 2010;2010:5549-52. 101. Daly JJ, Cheng R, Rogers J, Litinas K, Hrovat K, Dohring M. Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 2009;33:203-11. 102. Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol 2013;74:100-8. 103. Page SJ, Fulk GD, Boyne P. Clinically important differences for the upper-extremity fugl-meyer scale in people with minimal to moderate impairment due to chronic stroke. Phys Ther 2012;92: 791-8. 104. Marone G, Pisotta I, Pichiorri F, et al. Proof of principle of a braincomputer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability. Arch Phys Med Rehabil 2015;96:S71-8. 105. Meng J, Yao L, Sheng X, Zhang D, Zhu X. Simultaneously optimizing spatial spectral features based on mutual information for EEG classification. IEEE Trans Biomed Eng 2015;62:227-40. 106. Loza C, Philips G, Hazrati M, Daly JJ, Principe J. Classification of hand movement direction based on EEG high-gamma activity. In: Engineering in Medicine and Biology Society, 36th Annual International Conference of the IEEE; 2014; Chicago, IL. p 6509-12. 107. Liu Y, Li M, Zhang H, et al. A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training. J Neurosci Methods 2014;222:238-49. 108. Yeh WL, Huang YC, Chiou JH, Duann JR, Chiou JC. A self produced mother wavelet feature extraction method for motor imagery brain-computer interface. Conf Proc IEEE Eng Med Biol Soc 2013; 2013:4302-5. 109. Zhang H, Liu Y, Liang J, Cao J, Zhang L. Gaussian mixture modeling in stroke patients’ rehabilitation EEG data analysis. Conf Proc IEEE Eng Med Biol Soc 2013;2013:2208-11. 110. Marquez J, van Vliet P, McElduff P, Lagopoulos J, Parsons M. Transcranial direct current stimulation (tDCS): does it have merit in stroke rehabilitation? A systematic review. Int J Stroke 2013 Oct 22. [Epub ahead of print] 111. Prichard G, Weiller C, Fritsch B, Reis J. Effects of different electrical brain stimulation protocols on subcomponents of motor skill learning. Brain Stimul 2014;7:532-40. 112. Galea JM, Vazquez A, Pasricha N, de Xivry JJ, Celnik P. Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. Cereb Cortex 2011;21:1761-70. 113. Herzfeld DJ, Pastor D, Haith AM, Rossetti Y, Shadmehr R, O’Shea J. Contributions of the cerebellum and the motor cortex to acquisition and retention of motor memories. Neuroimage 2014;98: 147-58. 114. Reis J, Fritsch B. Modulation of motor performance and motor learning by transcranial direct current stimulation. Curr Opin Neurol 2011;24:590-6. 115. Stagg CJ, Best JG, Stephenson MC, et al. Polarity-sensitive modulation of cortical neurotransmitters by transcranial stimulation. J Neurosci 2009;29:5202-6. 116. Miniussi C, Harris JA, Ruzzoli M. Modelling non-invasive brain stimulation in cognitive neuroscience. Neurosci Biobehav Rev 2013; 37:1702-12. 117. Folcher M, Oesterle S, Zwicky K, et al. Mind-controlled transgene expression by a wireless-powered optogenetic designer cell implant. Nat Commun 2014;5:5392.
INTRODUCTION
Brain-Computer Interface: Current and Emerging Rehabilitation Applications Janis J. Daly, PhD, MS,a,b,c,d Jane E. Huggins, PhDe From the aBrain Rehabilitation Research Program, McKnight Brain Institute, University of Florida, Gainesville, FL; bDepartment of Neurology, College of Medicine, University of Florida, Gainesville, FL; cBrain Rehabilitation Research Center of Excellence; dNorth Florida/South Georgia Veterans Affairs Medical Center, Gainesville, FL; and eDepartment of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, and Program of Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI.
Abstract A formal definition of brain-computer interface (BCI) is as follows: a system that acquires brain signal activity and translates it into an output that can replace, restore, enhance, supplement, or improve the existing brain signal, which can, in turn, modify or change ongoing interactions between the brain and its internal or external environment. More simply, a BCI can be defined as a system that translates “brain signals into new kinds of outputs.” After brain signal acquisition, the BCI evaluates the brain signal and extracts signal features that have proven useful for task performance. There are 2 broad categories of BCIs: implantable and noninvasive, distinguished by invasively and noninvasively acquired brain signals, respectively. For this supplement, we will focus on BCIs that use noninvasively acquired brain signals. Archives of Physical Medicine and Rehabilitation 2015;96(3 Suppl 1):S1-7 ª 2015 by the American Congress of Rehabilitation Medicine
A formal definition of brain-computer interface (BCI) is as follows: “a system that measures central nervous system (CNS) activity and converts it into artificial output that replaces, restores, enhances, supplements, [informs], or improves natural CNS output and thereby changes the ongoing interactions between the CNS and its external or internal environment.”1(p3) More simply, a BCI can be defined as a system that translates “brain signals into new kinds of outputs.”1(p5) There are 2 major ways in which BCIs can be used. The first is straightforward and has been studied for >25 years; in this case, the BCI system acquires a brain signal and allows the user, through feedback, to engage the BCI output for control of the environment (light switch, temperature control) or communication devices. A second and newly emerging BCI application involves using the system as a motor learningeassist device. In this case, the BCI may enhance motor control recovery by demanding more focused attention or guiding activation or deactivation of brain signals.2 BCI research has experienced a recent exponential growth, which can be attributed to a number of the following factors: availability of rapid, real-time sophisticated signal processing methods; a greater understanding of the characteristics and uses of brain signals; an Presented to the National Institutes of Health, National Science Foundation, and other organizations (for a full list, see http://bcimeeting.org/2013/sponsors.html), June 3-7, 2013, Asilomar Conference Grounds, Pacific Grove, CA. Disclosures: none.
appreciation of the phenomenon of activity-dependent brain plasticity; and a growing dissatisfaction with current rehabilitation methods and the need for improved methods for recovery of function for those with persistent motor impairment.2 Brain signals can be acquired in a number of forms, including electrical (eg, electroencephalography [EEG]) or magnetic fields (eg, functional magnetic resonance imaging [fMRI]) or functional near infrared spectroscopy (fNIRS). It is important for those of us who are clinicians and clinicianscientists to be informed about the development and capability of BCIs because these systems have potential to enhance rehabilitation methods. Even more importantly, it is critical for us to participate in the design and development of these systems so that BCI system designs are grounded in the needs of patients, framed within feasible technical interfaces, and constructed for practical delivery in a clinical environment. To that end, we present this supplement: BrainComputer Interface: Current and Emerging Rehabilitation Applications. Within this supplement, we are providing articles that arose from presentations at the 2013 International Brain-Computer Interface Meeting, which was held June 3 through June 7, 2013, at the Asilomar Conference Center in Pacific Grove, California. The 2013 BCI meeting was the fifth in the international BCI meeting series, with past meetings in 1999, 2002, 2005, and 2010. The purpose of the international BCI meeting series is to bring
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together the diverse contributors to BCI research and development in a distinctive retreat-style meeting that encourages interaction, collaboration, and discussion. Therefore, the international BCI meeting series strives to push the BCI field forward, encouraging growth and translation of BCIs from the laboratory to the clinic. The 2013 meeting was supported by the National Institutes of Health and National Science Foundation and other governmental and private sponsors (further sponsor information available at http://bcimeeting. org/2013/sponsors.html). The meeting drew scientists from 29 countries, representing 165 research groups, with a total of 301 attendees, of whom 37% were students or postdoctoral fellows. There were >200 extended abstracts submitted for peer review, from which 25 were selected for oral presentation (individual index abstracts: http://bcimeeting.org/2013/researchsessions.html), and 181 were selected for poster presentation (individual index abstracts: http:// bcimeeting.org/2013/posters.html). Accepted abstracts were published in open-access conference proceedings (http://castor.tugraz.at/ doku/BCIMeeting2013/BCIMeeting2013_all.pdf). The retreat-style format featured 19 highly interactive workshops3 and an exhibit hall with formal poster session and technology demonstrations. The 2013 BCI meeting theme was Defining the Future. Compared with prior BCI conferences, attendance included an increased representation of clinicians, clinician-scientists, and people with disabilities. There were a number of firsts for the meeting series. First, the planning committee was composed of BCI researchers from around the world. Second, both the planning committee and conference participants included people with severe disabilities who need assistive technology for communication. A woman with amyotrophic lateral sclerosis (ALS) who uses assistive technology for communication served on the program committee (http:// bcimeeting.org/2013/meetinginfo.html). She attended the meeting remotely; she participated in a panel discussion and provided a presentation at a virtual BCI user’s forum. This forum provided a venue by which BCI users could speak directly to the conference attendees. A man with brainstem stroke attended the meeting with his caregivers, presenting both in a workshop and in the virtual user’s forum. Both are coauthors on an article in this supplement4 summarizing the experiences of BCI users with the current state of BCI technology. A third new development was that attendees at the 2013 BCI meeting voted to establish a Brain-Computer Interface Society, which will plan and oversee the 6th International BCI Meeting to be held in 2016. Fourth, there was an increase in the number of venues for dissemination of results. This supplement contains articles with a clinical or patient experience focus. A special section in the Journal of Neural Engineering published articles with an engineering focus.5 A summary of the conference workshops was published in the newly established Brain-Computer Interfaces journal.3 The articles in this supplement provide examples of work conducted using a variety of BCI technology applications, including communication, leisure activities, and motor learning.
List of abbreviations: AAC ALS BCI CNS EEG fMRI fNIRS tDCS TMS
augmentative and alternative communication amyotrophic lateral sclerosis brain-computer interface central nervous system electroencephalography functional magnetic resonance imaging functional near infrared spectroscopy transcranial direct current stimulation transcranial magnetic stimulation
Communication Problem Communication is an essential function for health care,6,7 function, and quality of life.8 For those with neuromuscular impairments and difficulty with writing or speaking, augmentative and alternative communication (AAC) devices can compensate and provide device-assisted communication.6 Most currently available AAC devices are controlled by available physical movements, and in the presence of volitional movement, they work well for performing a simple task. However, there are limitations to currently available AAC devices. First, the capability of currently available AAC devices can be overwhelmed by task complexity or by the simultaneous task demands of a given function. Second, some individuals do not possess the required physical capability to control an AAC device, and others have progressive diseases which eventually preclude their use of any physical movement to control communication devices. Therefore, the inability of some people to operate AAC devices is of particular concern6 and represents an area of vital need. A study on end-of-life decisions by people with ALS7 quoted a participant as saying “as long as I can properly communicate with my voice, my eyes or a machine or whatever, I want to have a respirator.But as soon as I can no longer communicate, that’s it! I don’t want anything else to be done.”(p210)
Role of BCIs in rehabilitation In contrast with most available AAC devices, BCIs can be controlled through the direct use of brain signal, bypassing the need for volitional muscle activity as a control paradigm. For a number of years,9,10 potential BCI users and caregivers have expressed the importance of BCIs for communication. In a focus group of potential BCI users with ALS and their caregivers, one caregiver described the promise of BCI as follows: “I just think it is wonderful that you can give someone a voice who is losing theirs.”11(p523) BCIs have been developed and tested for use in controlling devices for communication. BCIs are most appropriate and most needed by people with few other options for control of assistive technology. These include people with late-stage ALS, people with disorders of consciousness who show signs of cognitive awareness but lack other means of communication, and other populations of people who cannot reliably operate physical interfaces or eye gaze systems to access assistive technology. In this supplement, Ku¨bler et al12 discuss a decision process for determining who should participate in in-home BCI research studies, considering participant characteristics, support structure, and environmental factors. To date, BCIs have primarily been used in the laboratory or in controlled research studies. However, one commercial BCI device is now available.a Therefore, as discussed by Hill et al13 in this supplement, critical issues remain for the widespread adoption of BCIs as practical AAC devices for clinical use. Peters’ article4 describes both the promise and shortcomings of BCI as a communication device and compares BCI to conventional assistive technology solutions. BCIs are increasingly being integrated with other commercial assistive technology on an experimental basis14 and can form an interface that is based on brain signals and is incorporated within the framework of other existing assistive technology, increasing the accessibility of such devices.15 There is a growing awareness that BCIs can be used in combination with physical input signals if the patient has such signals available, a concept described as a hybrid BCI design.16 In this supplement, Schettini et al17 investigate the www.archives-pmr.org
Brain-computer interface: current and emerging applications design of a hybrid BCI to provide people with ALS with a system that will adapt to meet their needs as their ALS progresses. People with ALS have been the predominate user group for noninvasive BCI communication systems to date,15,18-20 and demonstrations of home use have been published.21,22 Hill et al13 draw on examples from a study of in-home BCI use by people with ALS, and most of the participants in the virtual BCI user’s forum had ALS.4 For patients diagnosed with disorders of consciousness, BCI may offer the only opportunity of demonstrating awareness. A recent review showed that 4 (17%) of 24 patients who had been diagnosed as being in a vegetative state were not only consciously aware but could answer yes or no questions.23 BCIs offer a method to evaluate awareness and to restore a communication channel. BCI use for disorders of consciousness is an emerging field of great importance. Studies in this area include BCIs using auditory-, tactile-, and motor imageryebased designs.23-32 In this supplement, Coyle et al33 describe a study of BCI use with people in a minimally conscious state, demonstrating that it is possible to learn to use a BCI device even in the absence of any other method of communication. People with spinal cord injury or brainstem stroke represent other populations for whom BCIs may be important and useful. BCI studies with people with spinal cord injury have included both computer access tasks34,35 and exoskeleton control.35 In this supplement, Huggins et al explore the BCI design priorities for people with SCI for the purpose of setting research goals and evaluating how close BCIs are to providing benefit for this population. Also, Riccio et al,36 in this supplement, evaluated a hybrid BCI design using a BCI in conjunction with a muscle activity switch for error correction in a study that included people with brainstem stroke.
Limitations The primary concern for BCIs as an AAC is the reliance of most BCIs on visual presentation of options, with an associated concern about possible reliance on eye gaze.37 Indeed, some people with ALS have trouble not only controlling eye gaze, but also with keeping their eyes open,38 which poses potential problems in using a visual display to view options and feedback results. To address this problem, BCIs that operate with covert attention have been developed, proving that eye gaze control is not required for BCI use.39-43 However, covert attention is associated with lower accuracy of BCI performance.44 A BCI with a visual display has even been designed for use in an eyes-closed condition by using lights that are visible through the eyelids.45 Alternative BCI designs using auditory32,43,46-49 and even tactile50-53 displays of information are under development. In this issue, auditory BCI feedback is mentioned by Ku¨bler et al12 as part of the considerations for selecting BCI participants, and it is also used in Coyle’s study. One other consideration is the ability to learn to use a BCI. There have been some reports of greater variability in success at learning to use a BCI device among those with disabilities compared with nonimpaired adults.54-56 However, most people are able to learn to use at least one of the BCI designs for communication.57,58 Overall, BCIs for communication have been successful in the laboratory and for long-term functional use.22,59
Leisure activities Problem Disability touches every aspect of human life; therefore, assistive technology is needed to provide access to all types of activities www.archives-pmr.org
S3 that contribute to quality of life.60 Assistive technology can enable participation in sport and leisure activity, providing connection to others and improvements in self-esteem and self-confidence.61,62
Potential BCIs have the potential to provide an array of leisure pursuits, and applications in this area are just emerging. BCIs have enough flexibility to provide useful computer access, which can be used for leisure activities.15 Peters et al4 describe the need for this type of flexibility, as articulated by potential BCI users. In Huggins’ survey,63 respondents also suggested game playing as a desirable BCIassisted activity. We can also note that some leisure activities are served by existing BCIs, which provide for environmental control functions (eg, television control). BCIs based on sensorimotor brain activity have used games as part of user training protocols.64 BCI applications for leisure activities have also been developed either as primary applications or additional functions for BCIs.65-67 In this supplement and elsewhere, Holz et al67 describe the positive impact of a BCI-operated painting program.
Recovery of function Problem It is important to develop innovative methods (eg, BCI neural feedback systems) to restore motor function to those with neural injury or disease because current standard care and research approaches do not restore normal function. Generally, there are 2 research approaches currently studied for treating motor impairment after neurologic injury or disease: peripherally directed treatments (eg, exercise, exercise-assisted technologies68-73) or centrally directed treatments involving brain stimulation methods, such as transcranial magnetic stimulation (TMS),74,75 transcranial electrical stimulation,75 or transcranial direct current stimulation (tDCS).75,76 Peripherally directed interventions are promising, for example, for stroke survivors. Interventions tested include robotics,69 functional electrical stimulation,71,73 coordination and functional task training,68,71,73 and body weightesupported treadmill training.70,71 Even for those with moderate to severe impairment, some studies show clinically and statistically significant gains in response to innovative treatment in the Fugl-Meyer lower-limb coordination scale71 and the Arm Motor Ability Test function score73 and significant gains in quality of life73 or in proxy function measures (eg, gait speed).70 Although promising, these peripherally directed interventions do not restore normal function in most study participants, and some participants do not respond to treatment. Therefore, it is critical to develop other more effective treatment methods. Although less studied than peripherally directed interventions, brain stimulation methods have been more recently applied to the problem of motor relearning for those with dyscoordination from neural injury or disease. The motor skill acquisition process (motor learning) is driven by neuroplasticity in a number of cortical centers, including the motor cortex,76,77 cerebellum,78-80 and cerebellar pathways through the thalamus to the cortex.81 Engagement of these neural centers and pathways during the motor learning process is thought to produce a motor memory, or motor engram, which includes the neural substrate engaged during the task.76 Brain stimulation methods hold some promise. However, as applied to the problem of motor relearning after neural injury or disease, results are currently mixed. In the study of stroke treatment, scientists are reporting transient (days to a few weeks) motor gains in
S4 response to the application of TMS, but it is difficult to evaluate the body of literature because of heterogeneity of both subject and intervention variables. These include time since stroke (acute/ chronic); repetitive TMS frequency variability (1e25Hz repetitive TMS); treatment duration variability (1e10 sessions); and measures of motor function for which the value is unknown regarding the minimally clinically significant improvement.74,82,83 In the application of tDCS, results are generally positive for improving motor function in older adults84,85 and stroke survivors.86,87 tDCS application for those with Parkinson disease ranges from mixed88 to no response on most motor measures.89 In this supplement, Ang et al90 present a study of the effects of tDCS on brain signals during training with an EEG-BCI; findings suggest that tDCS can enhance brain signal features that are used during EEG-BCI training, but further work will be needed to determine the actual role and benefit of both EEG-BCI and tDCS in physical recovery. Similar to the difficulties in evaluating response to rTMS treatment, the same or similar difficulties exist in evaluating response to tDCS. Further, reported results are not currently greater than peripherally directed treatments. Some have proposed that the problem arises from the variability of neural networks across individuals, leading to a lack of certainty as to whether the applied stimulus did not work well in general or only for a given individual (who did not exhibit change).75 One suggestion put forth for future work75 is to combine the new information regarding modeling the electrical fields induced by brain stimulation91 and the emerging information of cortical circuits92 to more specifically and accurately target brain stimulation.
Justification Given the promise of direct brain stimulation, but its limited results to date, it is important to continue to develop interventions for motor recovery that more directly target or engage the brain in the location where the pathology occurs. Given the individual variability of brain structure, neural networks, and alterations after brain injury or disease, one promising avenue of working directly with the brain itself is to use a neural feedback training system, which engages the individual’s central nervous system encompassing all its individual uniqueness. Biofeedback has been a useful intervention for a number of different types of health problems. Biofeedback systems can be based on noninvasively acquired brain signals, such as real-time fMRI, fNIRS, and EEG. The term “BCI” has been applied to these noninvasive biofeedback systems, which are dependent upon rapid brain signal processing that can be used in real time during training for cognition, emotion function, and motor learning; that is, no timeconsuming, off-line signal processing is conducted and motor learning fMRI-BCI has been tested in several therapeutic applications. For example, fMRI-BCI was used in a preliminary study to train those with schizophrenia to more normally control emotion through modulation of the left and right insula brain regions.93 Additionally, real-time fMRI-BCI was successfully used to reduce pain perception through modulation of the right anterior cingulate cortex.94 Studies of real-time fNIRS-BCI for application to motor function have focused on identification of usable signals in a BCI neural feedback system.95,96 EEG-BCI systems have been successfully used to reduce seizure activity97,98 and improve function in those with attention deficit hyperactivity disorder.99 With mixed success, EEG-BCI systems have been tested for use in motor learning after stroke.100,101 Recently, a randomized controlled trial was conducted to compare physical therapy arm coordination/ functional test training alone or in combination with EEG-BCI
J.J. Daly, J.E. Huggins training.102 Results showed a statistically significant additive advantage of the EEG-BCI treatment, according to the Fugl-Meyer arm coordination score; however, the absolute group difference was only 3 Fugl-Meyer arm coordination score points, which is not clinically significant.103 The Marone study,104 in this supplement, looks to the future with a study to test the feasibility of using noninvasive EEG-BCI systems in the clinic.
Future challenges and directions Although EEG-BCI proved clinically feasible for motor training, other factors must first be considered before clinical deployment should or can be realized, with efficacy of treatment being the foremost unproven factor. There are many sources of difficulty in developing an efficacious noninvasive brain neural feedback system. For example, real-time fMRI-BCI is not practical for daily biofeedback sessions. Real-time fNIRS-BCI and EEG-BCI are more practical and portable, but they depend on brain signals that are a summation of complex neural activity: the summated signals obfuscate the signal features that are likely most desirable and most reflective of the neural activity that should be targeted for treatment. In response to this difficulty, there is a growing body of literature that seeks to derive more specific meaning from EEG signals so that EEG-BCI can be more effectively applied to training recovery of motor function after neural injury or disease.105-109 At the same time, the future development of BCI feedback systems should leverage knowledge of the neural mechanisms of motor learning and how to engage those mechanisms specifically with BCI neural feedback systems. We can learn this process from the example provided in the study of tDCS brain stimulation. That is, Orban de Xivry and Shadmehr72 have synthesized a body of work and derived 3 major principles of tDCS and treatment responses as follows: (1) “Firing rates are increased by tDCS anodal polarization and decreased by cathodal polarization, accounting for the effect of tDCS on motor behaviors that do not involve learning”(p3381); (2) Anodal polarization strengthens newly formed associations, accounting for facilitation by tDCS of sequence-learning110 and “speed/accuracy trade-off skill,”111 requiring the formation of new patterns of motor activity”; and (3) “tDCS polarization modulates the memory of new/ preferred firing patterns, accounting for tDCS alteration of the rate of motor adaption.”112,113 Theories have been put forth to explain some of the positive response to tDCS; these theories include modulation of long-term potentiation,114 a decrease in gammaaminobutyric acid,115 and an increase or decrease in the level of noise in neural activity.116 However, none of these theories alone adequately explain the experimental results. Once proven, mechanisms of motor learning may form the basis for a BCI that could deliver neural regulators to a particular brain location or to specific neural substrates to enhance motor skill acquisition after neural injury or disease. Although seemingly impossible, others117 have recently designed and tested an EEG-BCI that uses the brain state in a mouse model to control a wireless-powered optogenetic designer cell implant for ultimate induction of synthetic interferon-beta (balances the expression of pro- and antiinflammatory agents in the brain and protects the brain). The application of brain stimulation and the use of brain neural feedback training both depend on our knowledge of motor skill acquisition, the function of the neural centers involved, the mechanisms of the interventions, and the mechanisms of neural recovery of motor control. The synthesis of these discoveries will provide the platform for future BCIs. www.archives-pmr.org
Brain-computer interface: current and emerging applications
Conclusions BCIs are emerging as interventions to restore function through assistive technology and to promote recovery. Success in these applications has been observed in the laboratory and in limited, in-home studies for communication applications. However, BCI technology is still clinically immature, and further development with the participation of clinicians and clinician-scientists is needed before the full potential of BCIs for rehabilitation can be realized.
Supplier a. IntendiX; g.tec Medical Engineering GmbH.
Keywords Brain-computer interfaces; Rehabilitation
Corresponding author Janis J. Daly, PhD, MS, Director, Brain Rehabilitation Research Center of Excellence, North Florida/South Georgia Veterans Affairs Medical Center, 151-A, 1601 SW Archer Rd, Gainesville, FL 32608. E-mail address: [email protected].
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